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ETH Zürich AI in the Sciences and Engineering 2024
*Course Website* (links to slides and tutorials): https://www.camlab.ethz.ch/teaching/ai-in-the-sciences-and-engineering-2024.html
Lecturers: Dr. Ben Moseley and Prof. Siddhartha Mishra
▬ Lecture Content ▬▬▬
0:00 - The impact of AI in science
5:26 - Why is AI so popular today?
11:10 - Grand scientific challenges
18:11 - Flaws of deep learning
25:09 - Scientific machine learning (SciML)
28:15 - Course learning objectives & timeline
34:52 - Key scientific tasks
56:23 - Examples of SciML algorithms
1:03:52 - Different classes of SciML algorithms
▬ Course Overview ▬▬▬
Lecture 1: Course Introduction youtube.com/watch?v=LkKvhvsf6jY&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 2: Introduction to Deep Learning Part 1 youtube.com/watch?v=OXmLwCQA7F4&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 3: Introduction to Deep Learning Part 2 youtube.com/watch?v=z3tQaNOwQqM&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 4: Importance of PDEs in Science youtube.com/watch?v=UiZxDRBd0Q8&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 5: Physics-Informed Neural Networks – Introduction youtube.com/watch?v=D-F7BYRhAkQ&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 6: Physics-Informed Neural Networks – Limitations and Extensions Part 1 youtube.com/watch?v=S11QK8baGVI&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 7: Physics-Informed Neural Networks – Limitations and Extensions Part 2 youtube.com/watch?v=NFtE1pyD5LA&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 8: Physics-Informed Neural Networks – Theory Part 1 youtube.com/watch?v=AaChPylEH6U&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 9: Physics-Informed Neural Networks – Theory Part 2 youtube.com/watch?v=FqdJ2Jx9MVc&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 10: Introduction to Operator Learning Part 1 youtube.com/watch?v=yhHhMmiNl_g&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 11: Introduction to Operator Learning Part 2 youtube.com/watch?v=lEUgPvDi5O8&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 12: Fourier Neural Operators youtube.com/watch?v=b96wRdjH1Lg&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 13: Spectral Neural Operators and Deep Operator Networks youtube.com/watch?v=BxklDO0TMlA&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 14: Convolutional Neural Operators youtube.com/watch?v=5XaLKR08TwI&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 15: Time-Dependent Neural Operators youtube.com/watch?v=u1KFcAvjyCI&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 16: Large-Scale Neural Operators youtube.com/watch?v=FPXW9MxjV48&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 17: Attention as a Neural Operator youtube.com/watch?v=wJSgLRiU7D4&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 18: Windowed Attention and Scaling Laws youtube.com/watch?v=YtJhReM5bHY&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 19: Introduction to Hybrid Workflows Part 1 youtube.com/watch?v=fJbt6VKYycA&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 20: Introduction to Hybrid Workflows Part 2 youtube.com/watch?v=h8BH-6tjecc&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 21: Neural Differential Equations youtube.com/watch?v=jnjYsm4NjhE&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 22: Introduction to Diffusion Models youtube.com/watch?v=Tohlijxz3XQ&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 23: Introduction to JAX youtube.com/watch?v=0JsPcm_Vl1g&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 24: Symbolic Regression and Model Discovery youtube.com/watch?v=fe-PC4lw4yw&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 25: Applications of AI in Chemistry and Biology Part 1 youtube.com/watch?v=Y3rvzsW8TVU&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
Lecture 26: Applications of AI in Chemistry and Biology Part 2 youtube.com/watch?v=dDvTA_MoO_4&list=PLJkYEExhe7rYFkBIB2U5pf_RWzYnFLj7r
▬ Course Description ▬▬▬
AI is having a profound impact on science by accelerating discoveries across physics, chemistry, biology, and engineering. This course presents a highly topical selection of AI applications across these fields. Emphasis is placed on using AI, particularly deep learning, to understand systems modelled by PDEs, and key scientific machine learning concepts and themes are discussed.
▬ Course Learning Objectives ▬▬▬
- Aware of advanced applications of AI in the sciences and engineering
- Familiar with the design, implementation, and theory of these algorithms
- Understand the pros/cons of using AI and deep learning for science
- Understand key scientific machine learning concepts and themes